Students become familiar with the main concepts as well as with specific data analysis and machine learning techniques and become also familiar with many applications of it. They also acquire skills in reviewing scientific papers, giving scientific presentations and working in practice with data using various algorithms and relevant machine learning software.
Course Content (Syllabus)
Introduction, Regression, Decision Trees, Rule Learning, Instance - Based Learning, Bayesian Learning, Learning with Genetic Algorithms, Model Evaluation, Clustering, Association Rules, Feature Selection and Discretization, Ensemble Methods, Reinforcement Learning, Text Mining, Machine Learning Software (Python).
Machine Learning, Supervised learning, Unsupervised Learning, Classification, Regression
Additional bibliography for study
- Machine Learning, T. Mitchell, McGraw Hill, 1997.
- The Elements of Statistical Learning:Data Mining, Inference, and Prediction, T. Hastie, R. Tibshirani and J. Friedman, Springer, 2nd edition, 2009.
- Machine Learning:The Art and Science of Algorithms that Make Sense of Data, Peter Flach, Cambridge University Press, 2012.
- Introduction to Machine Learning with Python: A Guide for Data Scientists, Sarah Guido and Andreas C. Müller, O' Reilly Media, 2016.
- Data Mining, Practical Machine Learning Tools and Techniques with Java Implementation (second edition), Ian Witten & Eibe Frank, Morgan Kaufmann, 2005.
- Introduction to Machine Learning (Draft of incomplete Notes), Nils J. Nilsson, 2015 (https://ai.stanford.edu/~nilsson/mlbook.html)
- Introduction to Machine Learning, Ethem Alpaydin, The MIT Press, October 2004.
- Introduction to Data Mining. Pang-Ning Tan, Michael Steinbach and Vipin Kumar. Pearson Addison Wesley. 2005